Epileptic seizure detection by means of genetically programmed artificial features

被引：0

作者：

Firpi, Hiram ^{[1
]}

Goodman, Erik ^{[1
]}

Echauz, Javier ^{[1
]}

机构：

[1] Michigan State Univ, E Lansing, MI 48824 USA

来源：

GECCO 2005: Genetic and Evolutionary Computation Conference, Vols 1 and 2 | 2005年

关键词：

epilepsy; seizure detection; feature extraction; state-space reconstruction; genetic programming;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we describe a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbor classifier to automatically create artificial features-features that are computer-crafted and may not have a known physical meaning-directly from the reconstructed statespace trajectories of the EEG signals that reveal patterns indicative of epileptic seizure onset. The algorithm was evaluated in three patients and validation experiments were carried out using 267.6 hours of EEG recordings. The results with the artificial features compare favorably with previous benchmark work that used a handcrafted feature.

引用

页码：461 / 466

页数：6

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